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Record W4308518092 · doi:10.3390/technologies10060113

Modular Multi-Input DC/DC Converter for EV Fast Charging

2022· article· en· W4308518092 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTechnologies · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designConvertersPower (physics)Battery (electricity)MATLABElectrical engineeringForward converterBoost converterBuck converterComputer scienceVoltageElectronic engineeringTopology (electrical circuits)EngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a modular multi-input, single output DC/DC converter is proposed to enhance the energy management of a fast-charging station for electric vehicles (EVs). The proposed bidirectional converter can work in different modes of operation with fewer components and a modular design to extend the input power sources and increase the charging power rate. The converter has several merits compared to the conventional converters, such as centralizing the control, reducing power devices, and reducing power conversion stages. By using MATLAB/Simulink, the converter was tested in many operation modes and was used to charge a Nissan Leaf EV’s battery (350 V, 60 Ah) from hybrid sources simultaneously and individually in power up to (17 kW). In addition, it was tested on a hardware scale at a low power rate (100 W) for the validation of the simulation work and the topology concept. In addition, its different losses and efficiency were calculated during the different operation modes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.270
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it